Oriented Aggregation of Silicalite-1 Subcrystals into MFI Zeolite Crystals
Bibliographic record
Abstract
Precursor nanoparticles that form spontaneously on hydrolysis of silica source in aqueous solutions of tetrapropylammonium (TPA) hydroxide evolve to (TPA)- silicalite-1, a molecular-sieve crystal that serves as a model for the self-assembly of porous inorganic materials in the presence of organic structure-directing agents. The structure and role of these nanoparticles are of practical significance for the fabrication of 3-dimensional (3D) ordered porous materials and molecular-sieve films, but still remain elusive. Here we show experimental findings of nanoparticle and crystal evolution by controllable manipulation of silicalite-1 synthesis. We have investigated detailed structural features of MFI precursors, which involves precise temperature control during synthesis, fine purification of 6–10 nm silicalite-1 subcrystals (SCs) as starting materials, and subsequent compression via an ice-templating method. SCs aggregate into MFI crystals with unidirectional orientation─indicating that SCs possess at least partial MFI structure and a slab-like morphology. This synthetic route is not only facile and environmentally friendly but also highly reproducible. We believe this method holds great promise for advancing the application of membrane-based separation processes in the natural gas industry and organic liquid separation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".